{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-title"
    ]
   },
   "source": [
    "# Image features exercise\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "We have seen that we can achieve reasonable performance on an image classification task by training a linear classifier on the pixels of the input image. In this exercise we will show that we can improve our classification performance by training linear classifiers not on raw pixels but on features that are computed from the raw pixels.\n",
    "\n",
    "All of your work for this exercise will be done in this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "is_executing": false
    },
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading extenrnal modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "source": [
    "## Load data\n",
    "Similar to previous exercises, we will load CIFAR-10 data from disk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "is_executing": false
    },
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "from cs231n.features import color_histogram_hsv, hog_feature\n",
    "\n",
    "def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=1000):\n",
    "    # Load the raw CIFAR-10 data\n",
    "    cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "\n",
    "    # Cleaning up variables to prevent loading data multiple times (which may cause memory issue)\n",
    "    try:\n",
    "       del X_train, y_train\n",
    "       del X_test, y_test\n",
    "       print('Clear previously loaded data.')\n",
    "    except:\n",
    "       pass\n",
    "\n",
    "    X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "    \n",
    "    # Subsample the data\n",
    "    mask = list(range(num_training, num_training + num_validation))\n",
    "    X_val = X_train[mask]\n",
    "    y_val = y_train[mask]\n",
    "    mask = list(range(num_training))\n",
    "    X_train = X_train[mask]\n",
    "    y_train = y_train[mask]\n",
    "    mask = list(range(num_test))\n",
    "    X_test = X_test[mask]\n",
    "    y_test = y_test[mask]\n",
    "    \n",
    "    return X_train, y_train, X_val, y_val, X_test, y_test\n",
    "\n",
    "X_train, y_train, X_val, y_val, X_test, y_test = get_CIFAR10_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "source": [
    "## Extract Features\n",
    "For each image we will compute a Histogram of Oriented\n",
    "Gradients (HOG) as well as a color histogram using the hue channel in HSV\n",
    "color space. We form our final feature vector for each image by concatenating\n",
    "the HOG and color histogram feature vectors.\n",
    "\n",
    "Roughly speaking, HOG should capture the texture of the image while ignoring\n",
    "color information, and the color histogram represents the color of the input\n",
    "image while ignoring texture. As a result, we expect that using both together\n",
    "ought to work better than using either alone. Verifying this assumption would\n",
    "be a good thing to try for your own interest.\n",
    "\n",
    "The `hog_feature` and `color_histogram_hsv` functions both operate on a single\n",
    "image and return a feature vector for that image. The extract_features\n",
    "function takes a set of images and a list of feature functions and evaluates\n",
    "each feature function on each image, storing the results in a matrix where\n",
    "each column is the concatenation of all feature vectors for a single image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "is_executing": false
    },
    "scrolled": true,
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done extracting features for 1000 / 49000 images\n",
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      "Done extracting features for 48000 / 49000 images\n",
      "Done extracting features for 49000 / 49000 images\n"
     ]
    }
   ],
   "source": [
    "from cs231n.features import *\n",
    "\n",
    "num_color_bins = 10 # Number of bins in the color histogram\n",
    "feature_fns = [hog_feature, lambda img: color_histogram_hsv(img, nbin=num_color_bins)]\n",
    "X_train_feats = extract_features(X_train, feature_fns, verbose=True)\n",
    "X_val_feats = extract_features(X_val, feature_fns)\n",
    "X_test_feats = extract_features(X_test, feature_fns)\n",
    "\n",
    "# Preprocessing: Subtract the mean feature\n",
    "mean_feat = np.mean(X_train_feats, axis=0, keepdims=True)\n",
    "X_train_feats -= mean_feat\n",
    "X_val_feats -= mean_feat\n",
    "X_test_feats -= mean_feat\n",
    "\n",
    "# Preprocessing: Divide by standard deviation. This ensures that each feature\n",
    "# has roughly the same scale.\n",
    "std_feat = np.std(X_train_feats, axis=0, keepdims=True)\n",
    "X_train_feats /= std_feat\n",
    "X_val_feats /= std_feat\n",
    "X_test_feats /= std_feat\n",
    "\n",
    "# Preprocessing: Add a bias dimension\n",
    "X_train_feats = np.hstack([X_train_feats, np.ones((X_train_feats.shape[0], 1))])\n",
    "X_val_feats = np.hstack([X_val_feats, np.ones((X_val_feats.shape[0], 1))])\n",
    "X_test_feats = np.hstack([X_test_feats, np.ones((X_test_feats.shape[0], 1))])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train SVM on features\n",
    "Using the multiclass SVM code developed earlier in the assignment, train SVMs on top of the features extracted above; this should achieve better results than training SVMs directly on top of raw pixels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true,
    "tags": [
     "code"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "iteration 1300 / 1500: loss 8.999995\n",
      "iteration 1400 / 1500: loss 8.999995\n",
      "lr 1.000000e-09 reg 5.000000e+04 train accuracy: 0.103204 val accuracy: 0.091000\n",
      "lr 1.000000e-09 reg 5.000000e+05 train accuracy: 0.095102 val accuracy: 0.084000\n",
      "lr 1.000000e-09 reg 5.000000e+06 train accuracy: 0.146286 val accuracy: 0.151000\n",
      "lr 1.000000e-08 reg 5.000000e+04 train accuracy: 0.111796 val accuracy: 0.102000\n",
      "lr 1.000000e-08 reg 5.000000e+05 train accuracy: 0.340551 val accuracy: 0.328000\n",
      "lr 1.000000e-08 reg 5.000000e+06 train accuracy: 0.415816 val accuracy: 0.403000\n",
      "lr 1.000000e-07 reg 5.000000e+04 train accuracy: 0.412469 val accuracy: 0.421000\n",
      "lr 1.000000e-07 reg 5.000000e+05 train accuracy: 0.414694 val accuracy: 0.426000\n",
      "lr 1.000000e-07 reg 5.000000e+06 train accuracy: 0.378796 val accuracy: 0.381000\n",
      "best validation accuracy achieved during cross-validation: 0.426000\n"
     ]
    }
   ],
   "source": [
    "# Use the validation set to tune the learning rate and regularization strength\n",
    "\n",
    "from cs231n.classifiers.linear_classifier import LinearSVM\n",
    "\n",
    "learning_rates = [1e-9, 1e-8, 1e-7]\n",
    "regularization_strengths = [5e4, 5e5, 5e6]\n",
    "\n",
    "results = {}\n",
    "best_val = -1\n",
    "best_svm = None\n",
    "\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Use the validation set to set the learning rate and regularization strength. #\n",
    "# This should be identical to the validation that you did for the SVM; save    #\n",
    "# the best trained classifer in best_svm. You might also want to play          #\n",
    "# with different numbers of bins in the color histogram. If you are careful    #\n",
    "# you should be able to get accuracy of near 0.44 on the validation set.       #\n",
    "################################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "for rate in learning_rates:\n",
    "    for reg in regularization_strengths:\n",
    "        svm=LinearSVM()\n",
    "        svm.train(X_train_feats,y_train,learning_rate=rate,reg=reg,num_iters=1500,verbose=True)\n",
    "        y_train_pred = svm.predict(X_train_feats)\n",
    "        y_val_pred = svm.predict(X_val_feats)\n",
    "        val=np.mean(y_val == y_val_pred)\n",
    "        if val>best_val:\n",
    "            best_svm=svm\n",
    "            best_val=val\n",
    "        results[(rate,reg)]=(np.mean(y_train == y_train_pred),val)\n",
    "\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "# Print out results.\n",
    "for lr, reg in sorted(results):\n",
    "    train_accuracy, val_accuracy = results[(lr, reg)]\n",
    "    print('lr %e reg %e train accuracy: %f val accuracy: %f' % (\n",
    "                lr, reg, train_accuracy, val_accuracy))\n",
    "    \n",
    "print('best validation accuracy achieved during cross-validation: %f' % best_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.425\n"
     ]
    }
   ],
   "source": [
    "# Evaluate your trained SVM on the test set\n",
    "y_test_pred = best_svm.predict(X_test_feats)\n",
    "test_accuracy = np.mean(y_test == y_test_pred)\n",
    "print(test_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 80 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# An important way to gain intuition about how an algorithm works is to\n",
    "# visualize the mistakes that it makes. In this visualization, we show examples\n",
    "# of images that are misclassified by our current system. The first column\n",
    "# shows images that our system labeled as \"plane\" but whose true label is\n",
    "# something other than \"plane\".\n",
    "\n",
    "examples_per_class = 8\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "for cls, cls_name in enumerate(classes):\n",
    "    idxs = np.where((y_test != cls) & (y_test_pred == cls))[0]\n",
    "    idxs = np.random.choice(idxs, examples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt.subplot(examples_per_class, len(classes), i * len(classes) + cls + 1)\n",
    "        plt.imshow(X_test[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls_name)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "### Inline question 1:\n",
    "Describe the misclassification results that you see. Do they make sense?\n",
    "\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neural Network on image features\n",
    "Earlier in this assigment we saw that training a two-layer neural network on raw pixels achieved better classification performance than linear classifiers on raw pixels. In this notebook we have seen that linear classifiers on image features outperform linear classifiers on raw pixels. \n",
    "\n",
    "For completeness, we should also try training a neural network on image features. This approach should outperform all previous approaches: you should easily be able to achieve over 55% classification accuracy on the test set; our best model achieves about 60% classification accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(49000, 155)\n",
      "(49000, 154)\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing: Remove the bias dimension\n",
    "# Make sure to run this cell only ONCE\n",
    "print(X_train_feats.shape)\n",
    "X_train_feats = X_train_feats[:, :-1]\n",
    "X_val_feats = X_val_feats[:, :-1]\n",
    "X_test_feats = X_test_feats[:, :-1]\n",
    "\n",
    "print(X_train_feats.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": [
     "code"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\programs\\anaconda\\envs\\cs231na1\\lib\\site-packages\\numpy\\core\\fromnumeric.py:86: RuntimeWarning: overflow encountered in reduce\n",
      "  return ufunc.reduce(obj, axis, dtype, out, **passkwargs)\n",
      "D:\\Codes\\PycharmProjects\\CS231nAssignment\\assignment1\\cs231n\\classifiers\\neural_net.py:75: RuntimeWarning: overflow encountered in matmul\n",
      "  scores = (X @ W1 + b1).clip(0) @ W2 + b2\n",
      "D:\\Codes\\PycharmProjects\\CS231nAssignment\\assignment1\\cs231n\\classifiers\\neural_net.py:91: RuntimeWarning: overflow encountered in subtract\n",
      "  scores -= np.max(scores, axis=1, keepdims=True)\n",
      "D:\\Codes\\PycharmProjects\\CS231nAssignment\\assignment1\\cs231n\\classifiers\\neural_net.py:91: RuntimeWarning: invalid value encountered in subtract\n",
      "  scores -= np.max(scores, axis=1, keepdims=True)\n",
      "D:\\Codes\\PycharmProjects\\CS231nAssignment\\assignment1\\cs231n\\classifiers\\neural_net.py:96: RuntimeWarning: overflow encountered in multiply\n",
      "  loss += 0.5 * reg * (np.sum(W1 * W1) + np.sum(W2 * W2))\n",
      "D:\\Codes\\PycharmProjects\\CS231nAssignment\\assignment1\\cs231n\\classifiers\\neural_net.py:96: RuntimeWarning: overflow encountered in double_scalars\n",
      "  loss += 0.5 * reg * (np.sum(W1 * W1) + np.sum(W2 * W2))\n"
     ]
    }
   ],
   "source": [
    "from cs231n.classifiers.neural_net import TwoLayerNet\n",
    "\n",
    "input_dim = X_train_feats.shape[1]\n",
    "hidden_dim = 500\n",
    "num_classes = 10\n",
    "\n",
    "net = TwoLayerNet(input_dim, hidden_dim, num_classes)\n",
    "best_net = None\n",
    "best_acc=0.0\n",
    "\n",
    "################################################################################\n",
    "# TODO: Train a two-layer neural network on image features. You may want to    #\n",
    "# cross-validate various parameters as in previous sections. Store your best   #\n",
    "# model in the best_net variable.                                              #\n",
    "################################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "best={}\n",
    "learning_rates = [1e-2 ,1e-1, 5e-1, 1, 5]\n",
    "regularization_strengths = [1e-3, 5e-3, 1e-2, 1e-1, 0.5, 1]\n",
    "\n",
    "for rate in learning_rates:\n",
    "    for reg in regularization_strengths:\n",
    "        net = TwoLayerNet(input_dim, hidden_dim, num_classes)\n",
    "        stats = net.train(X_train_feats, y_train, X_val_feats, y_val,num_iters=1500, batch_size=200,\n",
    "                learning_rate=rate, learning_rate_decay=0.95,reg=reg, verbose=False)\n",
    "        val_acc = (net.predict(X_val_feats) == y_val).mean()\n",
    "        if val_acc>best_acc:\n",
    "            best_acc=val_acc\n",
    "            best_net=net\n",
    "            best['reg']=reg\n",
    "            best['rate']=rate\n",
    "\n",
    "\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.58\n"
     ]
    }
   ],
   "source": [
    "# Run your best neural net classifier on the test set. You should be able\n",
    "# to get more than 55% accuracy.\n",
    "\n",
    "test_acc = (best_net.predict(X_test_feats) == y_test).mean()\n",
    "print(test_acc)"
   ]
  }
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